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Aiming at the problem that the cement production process is inherently affected by uncertainty, time delay, and strong coupling among variables, this paper proposed a novel soft sensor of free calcium oxide in a cement clinker. The model utilizes a dual-parallel integrated structure with an optimized integration of one-dimensional convolutional neural networks, long and short-term memory networks, graphical neural networks, and extreme gradient boosting. The proposed model can mitigate the risks associated with overfitting while incorporating the strengths of each individual model and excels in extracting both local and global features as well as temporal and spatial characteristics from the original time series data, ensuring its stability. The experimental results demonstrate that this dual-parallel integrated model exhibits superior robustness, predictive accuracy, and generalization capabilities when compared to single models or enhancements made to other deep learning algorithms. [ABSTRACT FROM AUTHOR]

Summary: Recently, a new intelligent algorithm called animal migration optimization (AMO) is proposed. The AMO algorithm, which is inspired by the behavior of animal migration, exhibits good performance on a series of benchmark functions whose dimensionality is less than or equal to 30. However, its performance deteriorates evidently when the dimensionality is more than 30. To overcome this drawback, we proposed an improved AMO (IAMO) with interactive learning mechanism in this article. First, an interactive learning mechanism is designed to improve the intelligence of AMO. During the the search process, individuals learn from each other by the exchange of information, and the search mode is adjusted dynamically. Second, a refined search method is adopted to enhance the search ability of AMO. Individuals will search around the current solutions in order to improve the quality of solutions. Third, a birth‐and‐death strategy is introduced to avoid local optimum. In addition, the property convergence of IAMO is analyzed theoretically. The proposed algorithm is validated on several standard benchmark functions whose dimensionality is 50, 100, and 150, respectively. The empirical results demonstrate that the performance of our proposed algorithm is promising. [ABSTRACT FROM AUTHOR]

Chimp optimization algorithm (ChOA), which simulates the social behaviors of chimps, is a novel swarm intelligence algorithm for solving global optimization problems. ChOA has the advantages of fast convergence and avoiding falling into local optimum. However, the global search capability is weakened and the time overhead is too large when solving complex optimization problems. In order to improve the overall performance of ChOA, a parallel chimp optimization algorithm based on tracking-learning and fuzzy opposition-learning behaviors (PChOA) is proposed in this paper. First, a tracking-learning behavior is designed to improve the search accuracy. Second, a fuzzy opposition-learning behavior is adopted to enhance the global search capability. Third, a parallel computing architecture is developed to accelerate computational speed. Moreover, the convergence of our proposed PChOA has been analyzed theoretically. To validate the effectiveness of PChOA, it is applied to solve classification problem. The experimental results demonstrate that the classification performance of our proposed algorithm outperforms six other state of the art algorithms on most used datasets. Meanwhile, the time overhead of PChOA is significantly reduced in the environment of parallel computing. When the number of processors is increased to 16, PChOA costs less time than NBTree which is the fastest comparison algorithm in the experiment. • We propose parallel chimp optimization algorithm with two learning behaviors for data classification. • We design a tracking-learning behavior which helps improve the search accuracy. • A fuzzy opposition-learning behavior is adopted to enhance the global search capability. • A parallel computing architecture is developed to accelerate computation speed. • The convergence is analyzed theoretically. [ABSTRACT FROM AUTHOR]